Real time control of connected industry devices such as mobile robots constituting Industrial Internet of Things (IIoT) has made time-sensitive communications over Wi-Fi increasingly important. In order to provide support for time-sensitive services over Wi-Fi, key features of the IEEE 802.11 standard such as restricted Target Wake Time (rTWT) and multi-user Orthogonal Frequency Division Multiple Access (OFDMA) can be exploited. However, even with rTWT and OFDMA, Time-Sensitive Networking (TSN) over Wi-Fi is still a challenge given the unpredictability of wireless channels and the increasing number of Wi-Fi-enabled devices. In this paper, we present a comprehensive network calculus-based analysis of the delay bounds achievable in Wi-Fi networks, leveraging these IEEE 802.11 enhancements alongside synchronized priority queuing via IEEE 802.1Qbv. We propose PONTE, a novel Fully Polynomial Time Approximation Scheme (FPTAS) scheduler that guarantees strict non-violation probabilities (e.g., 99.99%) for inelastic, time-critical traffic over an 802.11 wireless network. Our extensive analysis and simulations in an IIoT scenario demonstrate that PONTE effectively manages dense, mixed traffic flows, achieving TSN objectives with minimal impact on fairness. Our approach is two orders of magnitude faster than the state-of-the-art.

Industry 4.0 applications impose the challenging demand of delivering packets with bounded latencies via a wireless network. This is further complicated if the network is not dedicated to the time critical application. In this paper we use network calculus analysis to derive closed form expressions of latency bounds for time critical traffic when 802.11 Target Wake Time (TWT) and 802.1Qbv work together in a shared 802.11 network.

The advent of multi access edge computing~(MEC) will enable latency-critical applications such as cooperative adaptive cruise control (also known as platooning) to be hosted at the edge of the network. MEC-based platooning will leverage the coverage of the cellular infrastructure to enable inter-vehicular communications, potentially overcoming crucial problems of vehicular ad-hoc networks~(VANETs) such as non-trivial packet loss rates. However, MEC-based platooning will require the controller to be migrated to the most suitable positions at the network edge, in order to maintain low-latency connections as the platoon moves. In this paper, we propose a context-awareQ -Learning algorithm that carries out such migrations only as often as is necessary, and thereby reduces the additional delays implicit in application migration across MEC hosts. When compared to the state-of-the-art approach named FollowME, our scheme exhibits better compliance of vehicle speed and spacing values to preset targets, as well as a reduced statistical dispersion.

As a growing number of service and application providers choose cloud networks to deliver their services on a software-as-a-service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to meet service level agreements (SLAs). At the same time, they should guard against over-provisioning, which limits their capacity to accommodate more tenants. To this end, we propose Shortterm memory Q-Learning pRovisioning (SQLR, pronounced as “scaler”), a system employing a customized variant of the modelfree reinforcement learning algorithm. It can reuse contextual knowledge learned from one workload to optimize the number of virtual machines (resources) allocated to serve other workload patterns. With minimal overhead, SQLR achieves comparable results to systems where resources are unconstrained. Our experiments show that we can reduce the amount of provisioned resources by about 20% with less than 1% overall service unavailability (due to blocking), while delivering similar response times to those of an over-provisioned system.